tDKI-Net: a joint q-t space learning network for diffusion-time-dependent kurtosis imaging and Karger’s model fitting

Time-dependent diffusion magnetic resonance imaging (TDDMRI) is useful for non-invasive characterization of tissue microstructure. The TDDMRI models require both densely sampled q-space (b-value and diffusion direction) and t-space (diffusion time) data for microstructural fitting, leading to very time-consuming acquisition protocols. In this work, we presented a tDKI-Net to estimate diffusion kurtosis at multiple diffusion times, which was fed into the Karger model to obtain K0 and transmembrane exchange time, using downsampled q-space and t-space data. We tested the proposed network in the normal rat brains, as well as those in a rat model of Middle Cerebral Artery Occlusion.